Potential Risks from Cannabis- Infused Beverages: A Critical Review
Bibliographic record
Abstract
Although Canada legalized cannabis beverages in 2019, most available research on acute cannabis intoxication derives from dried flower and edible products. The distinct bioavailability and pharmacokinetic properties of phytocannabinoids ingested from beverages, however, contribute to significantly different acute and long-term effects that need to be better understood to ensure consumer safety. Objective: This review investigates existing cannabis beverage literature, with a particular focus on acute intoxication effects. Method: databases were systematically searched. A structured search generated 29 eligible studies, comprising studies of consumption patterns and beliefs, advertisements and marketing, acute effects in human models, and drink composition. Results: Human studies report aversive acute subjective and physiological effects induced by cannabis beverages in healthy, infrequent users. Beverages also showed inaccurate cannabinoid labeling, posing potential risks to consumers. This review highlights the paucity and inconsistency of available research, further exacerbated by the sheer diversity of formulations investigated, while beginning to address some questions surrounding the safety and risks associated with cannabis beverages. Conclusions: Given the extensive differences in effects across cannabis-infused beverages, and the growing 'drinkables' market, it is essential that more studies directly examine both acute and long-term impacts of cannabis beverage consumption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".